Method for determining location and movement of a moving object

ABSTRACT

The present invention discloses a method for determining location and movement of a moving object. One embodiment of the method tracks the movement of a target during medical imaging scanning and transmits the position shift to the medical imaging scanning device in real time. The method includes the steps of projecting structured light on the target, receiving the reflection of structured light, converting the received structured light into spatial positions, and transmitting the positional shift to the medical imaging scanning device. The method further includes the step of adjusting the medical imaging scanning device in response to the positional change to increase accuracy.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation-In-Part claiming priority benefitfrom U.S. patent application Ser. No. 11/282,046 which was filed on Nov.16, 2005.

TECHNICAL FIELD OF THE INVENTION

The present invention relates, in general, to the field of motiontracking, and in particular to head tracking in a magnetic resonanceimaging application. In particular the invention teaches an apparatusand method to track the movement of a target in three-dimensional spaceduring medical imaging scanning using optical technology. The inventionfurther comprises an apparatus and method to use the head tracking datato control the magnetic field gradients and/or radio frequency fields ofthe magnetic resonance imaging instrument thereby maintaining activeimage registration during the scans.

BACKGROUND OF THE INVENTION

Computerized tomography (CT), magnetic resonance imaging (MRI), andpositron emission tomography (PET), coupled with developments incomputer-based image processing and modeling capabilities have led tosignificant improvements in the ability to visualize anatomicalstructures in human patients. This information has become invaluable inthe diagnosis, treatment, and tracking of patients. The technology hasbeen recently been expanded to be used in conjunction with real-timeinterventional procedures.

MRI is the method of creating images (referred to as MR images) of theinternal organs in living organisms. The primary purpose isdemonstrating pathological or other physiological alterations of livingtissues. MRI has also found many niche applications outside of themedical and biological fields such as rock permeability to hydrocarbonsand certain non-destructive testing methods such as produce and timberquality characterization. Superb image contrast for soft tissues andmillimeter scale spatial resolution has established MRI as a coreimaging technology in most medical centers. MRI is unique among imagingmodalities in that any one of a multitude of tissue properties can beextracted and highlighted.

The MRI process requires a highly accurate and stable target which toimage. This is a consequence of the process by which medical MRIfunctions. Medical MRI most frequently relies on the relaxationproperties of excited hydrogen nuclei in water. When the object to beimaged is placed in a powerful, uniform magnetic field, the spins of theatomic nuclei with non-zero spin numbers within the tissue all align inone of two opposite directions: parallel to the magnetic field orantiparallel.

The difference in the number of parallel and antiparallel nuclei is onlyabout one in a million. However, due to the vast quantity of nuclei in asmall volume, the nuclei sum to produce a detectable change in fieldstrength. The magnetic dipole moment of the nuclei then moves in agyrating fashion around the axial field. While the proportion is nearlyequal, slightly more nuclei are oriented at the low energy angle. Thefrequency with which the dipole moments process is called the Larmorfrequency. The tissue is then briefly exposed to pulses ofelectromagnetic energy (RF pulse) in a plane perpendicular to themagnetic field, causing some of the magnetically aligned hydrogen nucleito assume a temporary non-aligned high-energy state.

In order to selectively image the different voxels (3-D pixels) of thematerial in question, three orthogonal magnetic gradients are applied.The first is the slice selection, which is applied during the RF pulse.Next comes the phase encoding gradient, and finally the frequencyencoding gradient, during which the tissue is imaged. Most of the time,the three gradients are applied in the X, Y, and Z directions of themachine. As a consequence of this methodology, any small shift in theposition of the patient with respect to these fixed gradient axes willalter the orientations and positions of the selected slices.

In order to create an MR image, spatial information must be recordedalong with the received tissue relaxation information. For this reason,magnetic fields with an intensity gradient are applied in addition tothe strong alignment field to allow encoding of the position of thenuclei. A field with the gradient increasing in each of the threedimensional planes is applied in sequence. This information is thensubsequently subjected to a Fourier transformation by a computer thattransforms the data into the desired image and yields detailedanatomical information results.

With conventional anatomic MR imaging, the presence of moving biologicaltissue is problematic. The tissue produces image artifacts, degrades thequality of the images, and complicates the interpretation of MR images.The typical appearance of such image artifacts takes the form of“blurring,” or a characteristic “motion ghost” in the phase encodingdirection associated with incorrectly encoding the spatial frequenciesof a moving object that is assumed to be static.

The typical medical resolution is about 1 mm, while research models canexceed 0.1 mm. Through the process of MRI, anatomy can be defined ingreat detail, and several other biophysical and metabolic properties oftissue, including blood flow, blood volume, elasticity, oxygenation,permeability, molecular self-diffusion, anisotropy, and water exchangethrough cell membranes, can also be represented. Conventional anatomicalMR imaging uses this spin-echo, gradient-echo, and inversion recoverysequencing. There are other methods of MR that are currently being used,including magnetic resonance spectroscopy (MRS), apparent diffusioncoefficient (ADC) mapping, diffusion-weighted imaging (DWI) and itsderivatives of diffusion tensor imaging and tractography, perfusionimaging, permeability imaging, MR angiography (MRA), and functional MRI(fMRI). As the techniques of MR become more precise, there iscorresponding need for increased accuracy and the tracking of thepatient during the MR procedure. See E. Fukushima and S. B. W. Roeder,Experimental Pulse NMR Addison-Wesley, Reading, M A 1981; T. C. Farrar,An Introduction To Pulse NMR Spectroscopy Farragut Press, Chicago, 1987;R. C. Jennison, Fourier Transforms and Convolutions Pergamon Press, NY1961; E. O. Brigham, The Fast Fourier Transform Prentice-Hall, EnglewoodCliffs, NJ 1974; and A. Carrington A. D. McLachlan, Introduction ToMagnetic Resonance Chapman and Hall, London 1967 which are each herebyincorporated by reference.

Functional MRI (fMRI) measures signal changes in the brain that are dueto changing neural activity. This scan is completed at a low resolutionbut at a very rapid rate (typically once every 1-3 seconds). Increasesin neural activity cause changes in the MR signal via a mechanism calledthe BOLD (blood oxygen level-dependent) effect. Increased neuralactivity causes a corresponding increased demand for oxygen, which isresponded to by the vascular system, which increases the amount ofoxygenated relative to deoxygenated hemoglobin. Because deoxygenatedhemoglobin attenuates the MR signal, the vascular response leads to asignal increase that is related to the neural activity. The use of MRIto measure physiologic and metabolic properties of tissue non-invasivelyrequires dynamic imaging to obtain time-series data.

One example of the use of fMRI is to measure brain activity. This userelies on a well-established neurovascular coupling phenomenon thatresults in transient increases in blood flow, oxygenation, and volume inthe vicinity of neurons that are functionally activated above theirbaseline level. Signal changes due to the bloodoxygenation-level-dependent (BOLD) effect are intrinsically weak (onlyseveral percent signal change from baseline at 4.0 T or less). Inaddition, as BOLD imaging is typically coupled with a repetitivebehavioral task (e.g., passive sensory, cognitive, or sensorimotor task)to localize BOLD signals in the vicinity of neurons of interest, thereis significant potential for fMRI to be confounded by the presence ofsmall head motions. Specifically, such motion can introduce a signalintensity fluctuation in time due to intra-voxel movement of aninterface between two different tissues with different MR signalintensities, or an interface between tissue and air. Random head motiondecreases the statistical power with which brain activity can beinferred, whereas task-correlated motion cannot be easily separated fromthe fMRI signal due to neuronal activity, resulting in spurious andinaccurate images of brain activation. In addition, head motion cancause mis-registration between neuroanatomical MR and fMR images thatare acquired in the same examination session. This latter point isimportant because the neuroanatomical MRI data serve as an underlay forfMRI color maps, and mis-registration results in mis-location of brainactivity. An analogous problem exists for aligning anatomical andfunctional MR images performed on different days.

Lack of motion in current MRI examinations anatomic motion is not merelypreferred, but is instead absolutely essential. Most aspects of humanmotor system performance require the patient to execute a movement aspart of the behavioral task that is imaged to visualize brain activity.Movements can be very simple (e.g., self-paced finger tapping) or morecomplex (e.g., visually-guided reaching). Such examinations require boththat the desired movement is performed in a well-controlled orwell-quantified fashion, and also that the movement does not inducetask-correlated head motion that confounds the ability to observe brainactivity using fMRI. Perhaps the most complicated scenario involvescombining use of virtual reality (VR) technology with fMRI, to determinebrain activity associated with VR tasks for assessment andrehabilitation of impaired brain function. Such applications areimportant from the standpoint of “ecological validity” as they providethe opportunity to visualize brain activity associated with tasks thatgeneralize well to everyday behavior in the real 3D-world. For example,position tracking would be required to provide realistic visualrepresentation of a virtual hand operated by a data glove in a virtualenvironment.

The problem of motion tracking within an fMRI environment has been welldocumented in published medical literature describing various aspects ofmotion detection and quantitation. See Seto et al., NeuroImage 2001,14:284-297; Hajnal et al., Magn Res Med 1994, 31: 283-291; Friston etal., Magn Res Med 1996, 35:346-355; Bullmore et al., Human Brain Mapping1999, 7: 38-48; Bandettini et al., Magn Res Med 1993, 30:161-173; Cox.Comp Med Res 1996, 29:162-173; Cox et al., Magn Res Med 1999,42:1014-1018; Grootoonk et al., NeuroImage 2000, 11:49-57; Freire etal., IEEE Trans Med Im 2002, 21(5):470-484; Babak et al., Magn Res Im2001, 19:959-963; Voklye et al. 1999, Magn Res Med 41:964-972, which areeach incorporated by reference.

As the clinical applications of MRI expand, there is a concurrentrequirement for improved technology to visualize and determine theposition and orientation of moving objects in the imaging field.Improvements in position tracking technology are required to advance theresolution and quality of the MRI, including the ability to image theanatomy of a patent, the imaging of tissue functions, the use of MRIdata for other imaging modalities, and interventional applications.

For anatomical and functional MRI applications, as well asinterventional MRI, there is the additional need to register data fromother imaging modalities to provide comprehensive and complementaryanatomical and functional information about the tissue of interest. Theregistration is performed either to enable different images to beoverlaid, or to ensure that images acquired in different spatial formats(e.g., MRI, conventional x-ray imaging, ultrasonic imaging) can be usedto visualize anatomy or pathology in precisely the same spatiallocation. While some algorithms exist for performing such registrations,computational cost would be significantly reduced by developingtechnology that enables data from multiple different imaging modalitiesto be inherently registered by measuring the patient's orientation ineach image with respect to a common coordinate system.

By detecting, tracking, and correcting for changes in movement, dataacquisition can be synchronized to a specific target. As a consequence,MR data acquisition is gated to a specific position of the target, andby implication, to a specific position of a specific target region.

U.S. Pat. No. 6,067,465 to Foo, et al. discloses a method for detectingand tracking the position of a reference structure in the body using alinear phase shift to minimize motion artifacts in magnetic resonanceimaging. In one application, the system and method are used to determinethe relative position of the diaphragm in the body in order tosynchronize data acquisition to the same relative position with respectto the abdominal and thoracic organs to minimize respiratory motionartifacts. The time domain linear phase shift of the reference structuredata is used to determine its spatial positional displacement as afunction of the respiratory cycle. The signal from a two-dimensionalrectangular or cylindrical column is first Fourier-transformed to theimage domain, apodized or bandwidth-limited, converted to real, positivevalues by taking the magnitude of the profile, and then transformed backto the image domain. The relative displacement of a target edge in theimage domain is determined from an auto-correlation of the resultingtime domain information.

There is often a need in neuroimaging to look for changes in brainimages over long periods of time, such as the waxing and waning of MSlesions, progressive atrophy in a patient with Alzheimer's disease, orthe growth or remission of a brain tumor. In these cases, the ability todetermine the position of anatomy as a function of time is extremelyimportant to detect and quantify subtle changes. High-spatial resolutionis a basic requirement of 3D brain imaging data for patients withneurological disease, and motion artifacts a consequence of movementduring scanning pose a significant problem. If a patient does not staycompletely still during MR neuroimaging the quality of the MR scan willbe compromised.

Many of the advantages of MRI that make it a powerful clinical imagingtool are also valuable during interventional procedures. The lack ofionizing radiation and the oblique and multi-planar imaging capabilitiesare particularly useful during invasive procedures. The absence ofbeam-hardening artifacts from bone allows complex approaches to anatomicregions that may be difficult or impossible with other imagingtechniques such as conventional CT. Perhaps the greatest advantage ofMRI is the superior soft-tissue signal contrast available, which allowsearly and sensitive detection of tissue changes during interventionalprocedures.

MR is used for procedures such as “interventional radiology”, whereimages produced by an MRI scanner guide surgeons in a minimally invasiveprocedure. However, the non-magnetic environment required by thescanner, and the strong magnetic radiofrequency and quasi-static fieldsgenerated by the scanner hardware require the use of specializedinstruments. Exemplary of such endoscopic treatment devices are devicesfor endoscopic surgery, such as for laser surgery disclosed in U.S. Pat.No. 5,496,305 to Kittrell et al, and biopsy devices and drug deliverysystems, such as disclosed in U.S. Pat. No. 4,900,303 and U.S. Pat. No.4,578,061 to Lemelson.

Prior art attempts at tracking motion using cross-correlation and othersimple distance measurement techniques have not been highly effectivewhere signal intensities vary either within images, between images, orboth. U.S. Pat. No. 6,292,683 to Gupta et al. discloses a method andapparatus to track motion of anatomy or medical instruments between MRimages. The invention includes acquiring a time series of MR images of aregion of interest, where the region of interest contains the anatomy orstructure that is prone to movement, and the MR images contain signalintensity variations. The invention includes identifying a localreference region in the region of interest of a reference image andacquired from the time series. The local reference region of thereference image is compared to that of the other MR images and atranslational displacement is determined between the local referenceregion of the reference image and of another MR image. The translationaldisplacement has signal intensity invariance and can accurately trackanatomy motion or the movement of a medical instrument during aninvasive procedure. The translational displacement can be used to alignthe images for automatic registration, such as in myocardial perfusionimaging, MRA, fMRI, or in any other procedure in which motion trackingis advantageous. One of the problems with this invention, is that theapplication and implementation of this methodology has proven difficult.

Two implementations of this correction scheme have been disclosed. Thefirst is where a correlation coefficient is calculated and used todetermine the translational displacement, and one in which the imagesare converted to a binary image by thresholding (using signal intensitythresholds) and after computation of a filtered cross-correlation, asignal peak is located and plotted as the translational displacement.Examples of techniques using this approach are shown in U.S. Pat. No.5,947,900 (Derbyshire) and U.S. Pat. No. 6,559,641 (Thesen)

U.S. Pat. No. 6,516,213 to Nevo discloses a method and apparatus todetermine the location and orientation of an object, while the body isbeing scanned by magnetic resonance imaging (MRI). Nevo estimates thelocation and orientation of various devices (e.g., catheters, surgeryinstruments, biopsy needles) by measuring voltages induced bytime-variable magnetic fields in a set of miniature coils, saidtime-variable magnetic fields being generated by the gradient coils ofan MRI scanner during its normal imaging operation. However, unlike thepresent invention, the system disclosed by Nevo is not capable ofposition tracking when imaging gradients are inactive, nor is it capableof measurements outside the sensitive volume of the imaging gradients.

A subset of all of the above correction schemes is currentlyconventionally employed in fMRI. As in anatomical MRI, these schemesremain an incomplete solution to the problem and the search for improvedmotion suppression continues. Typically, fast imaging is employed to“freeze” motion within the fMRI acquisition time frame, in combinationwith use of head restraints to limit motion. It is still possible toachieve poor activation image quality if patients exhibittask-correlated motion on the order of 1 millimeter. This problem isparticularly manifest in specific patient populations (e.g. dementia,immediate post-acute phase of stroke). Furthermore, image-basedcoregistration algorithms suffer from methodological limitations.Consequently, the resulting co-registered images still can suffer fromresidual motion contamination that impairs the ability to interpretbrain activity.

Another method of tracking the position of a patient in an MRI isdisclosed in US Application 2005/0054910, published Mar. 10, 2005. Inthis approach, a reference tool is fixed to a stationary target as closeas possible to the centre of the sensitive measuring volume of anMRI-compatible camera system. There are several drawbacks of thisapproach, including the requirement of a second “tracking” componentthat must be calibrated with a dummy object, the position ambiguity dueto the configuration of this approach, and the inherent limitation ofthe resolution provided by this approach.

U.S. Pat. No. 6,879,160 to Jakab describes a system for combiningelectromagnetic position and orientation tracking with magneticresonance scanner imaging. Jakab discloses a system where the locationof a magnetic field sensor relative to a reference coordinate system ofthe magnetic resonance scanner is determined by a tracking device usinga line segment model of a magnetic field source and the signal from amagnetic field sensor. However, resolutions provided by the Jakabinvention are not as precise as is possible.

There is consequently a need for improved patient movement trackingtechniques in medical imaging. There is a need for improved patientmovement tracking that can function in adverse environments includinghigh strength magnetic and/or radio frequency fields without thetracking mechanism exerting it's own RF pulse or magnetic field. Thereis a need for improved patient movement tracking techniques that can beperformed in real time. In particular, but without limitation, there isa need for real time tracking of a patient's head position in a highfield strength fMRI without disrupting the scanning by the fMRI.

SUMMARY OF THE INVENTION

The present invention includes improvements to the field of trackingpatient movement in an MRI application. An apparatus and method aretaught to track the movement of a patient's head during medical imagingscanning using optical technology. Feedback control of the gradientand/or radiofrequency magnetic fields can provide real time correctionof imaging data.

The following terms should be given the following meanings:

“Cross-correlation”—Cross-correlation is meant to include the processused to calculate the geometric translation differences between twoseparate and independent images. This process also compares twosequences of images on element-by-element bases and can provide thepoint of peak of most similarity.

“Structured light”—Structured light is meant to include patterns oflight that are suitable for cross-correlation. Generally speaking thismay include a bundle of light rays that may be patterned or structuredin order to enhance the performance of an optical measurement.Typically, the encoding of structured light is predetermined, so thatthe record of optical data can be optimally processed for spatialmeasurements. Examples of structured light may include, but are notlimited to, amplitude encoding, phase encoding, or a chromatic (orcolor) encoding.

“Phase correlation”—Phase correlation is meant to include the method oftaking the Fourier Transform of two or more images and correlating therelative phases to find rotation or scale between them.

“Laser”—The term laser includes illumination sources of sufficientintensity to drive detector optics to get a result. The illuminationsources can include broadband sources such as incandescent lamps andflashbulbs. Narrowband sources are also included such as gas dischargelasers or solid-state compound ataxia lasers. Illumination sources canalso include LEDs and/or arrays of LEDs. Illumination sources canfurther include sources of selected wavelength ranges or groups ofranges.

One embodiment of the instant invention is a system that is taught usedin conjunction within an MRI machine that uses a predetermined patternplaced or projected onto a patient's head to track movement of a patientduring an MRI scan. Optical systems incorporating structured light and aprocessor record the position and movement of the pattern and are ableto perform mathematical analysis of the pattern to determine thepositional shift of the patient. Weighted averages, Fourier transforms,Hadamard matrices and cross-correlation of data related to X-Ytranslation, rotation and scaling of the image of the pattern are usedto analyze movement of the patient's head. Feedback related to themovement is provided to the MRI machine that allows for adjustments infocusing coils for real time tracking of the patient's movements duringthe MRI procedure. As a result, the MRI procedure becomes more accurateas it is adjusted for the patient's movements.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the features and advantages of thepresent invention, reference is now made to the detailed description ofthe invention along with the accompanying figures in which correspondingnumerals in the different figures refer to corresponding parts and inwhich:

FIG. 1 is example of pseudo Matlab code that can be used in the weightedaverage approach of comparing two images.

FIG. 2 is example of pseudo Matlab code that can be used in the crosscorrelation of two images.

FIG. 3 is illustration of the conversion between different coordinatesystems.

FIG. 4 is example of pseudo Matlab code that can be used in theFourier-Mellin approach of comparing two images.

FIG. 5 is a schematic illustration of a head tracking apparatuscomprising a light source, structured light generated between the lightsource and the conveying light path, an object to be imaged, and adetector array.

FIG. 6 is a schematic illustration of a head tracking apparatuscomprising a light source, structured light generated in the conveyinglight path, an object to be imaged, and a detector array.

FIG. 7 is a schematic illustration of a head tracking apparatuscomprising a light source, structured light that is generated betweenthe conveying light path and the object to be tracked, the object to beimaged, and a detector array.

FIG. 8 is a schematic illustration of a head tracking apparatuscomprising a light source, a structured light generator at the object tobe imaged, the object to be imaged, and a detector array.

FIG. 9 is a block diagram of a head tracking apparatus used to provideactive feedback to the measurement fields of the MRI.

FIG. 10 is a flow chart illustrating a method of head tracking.

FIG. 11 is an illustration of one predetermined target that can be usedto with structured light.

FIG. 12 is an illustration of a predetermined target being placed onto apatient's forehead.

FIG. 13 is an illustration of a predetermined target being projectedonto a patient's forehead.

FIG. 14 is a flow chart illustrating a method of providing activefeedback to the measurement fields of the MRI based on head trackingdata.

FIG. 15 is a flow chart illustrating a translation detection algorithm.

FIG. 16 is a flow chart illustrating a calibration algorithm.

FIG. 17 is a flow chart illustrating one preferred embodiment of imagecorrelation.

FIG. 18 is an illustration of a target comprised of three patterns madeup of different dyes irradiated by sources of different frequencies.

DETAILED DESCRIPTION OF THE INVENTION

While the making and using of various embodiments of the presentinvention are discussed in detail below, it should be appreciated thatthe present invention provides many applicable inventive concepts whichcan be embodied in a wide variety of specific contexts. The specificembodiments described herein are merely illustrative of specific ways tomake and use the invention and do not delimit the scope of theinvention.

In one embodiment of the instant invention is a system that is used inconjunction within an MRI machine that uses a predetermined patternplaced or projected onto a patient's head to track movement of a patientduring an MRI scan. Optical systems record the position and movement ofthe pattern and are able to perform mathematical analysis of the patternto determine the positional shift of the patient.

In this preferred embodiment, light is projected onto a target thatreflects some of the light into an optical receiver. One of theinnovations of the present inventions is the use of structured light.Structured light consists of an orderly pattern of rays of light that issuitable for cross-correlation. Examples of methods to create structuredlight include, but are not limited to, using of a laser to create aspeckle pattern, a spatial filter using a projector to convey thestructured light pattern (an example would be with use of a patternedslide), and using a light source directed towards an area with a knownpattern. Other examples are an array of light emitters either positionedas a projector towards the target or as a light emitting tag placed onthe target. Another example is a spatial light modulator used in thepath of projected light such as a liquid crystal display or a MEMSdevice. Chemically patterned light emitting tags can also be used.Examples of these devices are a light emitting tag containing patternscreated by phosphorescent paint, inks or dyes. Other examples includevarious fluorophores used in inks or dyes such as pthalacyamine andnapthacyanine. In embodiments where inks and dyes are used withfrequency shifting capabilities such as up converters and downconverters, illuminating light should match the frequencies at which thetag produces light in detectable levels.

Since motion detection was implemented using a cross-correlationalgorithm, any form of similarity within the structured light wouldadversely influence the robustness of the algorithm. Therefore, anyregularity or order in the pattern would produce multiple peaks in thecross-correlation thus making it difficult to decide upon the highestone. This embodiment avoids the problem of similarity within theprojected light source by the use of an optimized pattern of structuredlight.

Many algorithms and methods of signal processing can be employed by thepresent invention in order to determine and track movement of thestructured light received either from the target or from the structuredlight generator. The preferred embodiment uses weighted averages,cross-correlation, Fourier-Mellin, phase correlation and imagemaximization to determine movement. Of course, other signal processingmethods known in the art will suffice. A weighted average is one methodused to calculate X and Y translational motions. The method treats everyblack pixel as a one and every light pixel as a zero. A pixel isconsidered black if its RGB value exceeds a certain preset value. Inaddition, a pixel is considered white if its RGB value is lower than acertain preset value. The algorithm calculates the center of the image'simaginary weight in much the same way as a center of mass would becalculated. The algorithm calculates the weighted average of the columnsand rows. When the pattern translates in two-dimensional space, theweighted average stays at the same place within the pattern. This allowsfor the determination of the amount of translation that has occurredbetween the two images. One example of an implementation of thisweighted average approach in a software application such as Matlab isgiven in FIG. 1.

Standard cross-correlation is another method that can be used tocalculate the X and Y translation differences of the images.Cross-correlation compares two sequences of images of a single target onan element-by-element basis and is able to provide the point or peak of“most similarity”. By calculating the coordinates of this peak, it ispossible to find the translation between the two images.Cross-correlation of two images can be imagined as sliding one threedimensional image over another until a perfect fit it found. Thecross-correlation of two complex functions f(t) and g(t) of a realvariable t, denoted fHg is defined by the following equation where *denotes convolution and ƒ is the complex conjugate of f(t):

ƒ*g≡ ƒ(−t)*g(t),

One example of an implementation of this cross-correlation approach in asoftware application such as Matlab is given in FIG. 2.

In another embodiment, the cross-correlation of rotation is found byusing a Fourier-Mellin algorithm. Fourier-Mellin method transformsCartesian coordinates to polar coordinates and correlates the FourierTransform of the two images to find the angle of rotation. Oneillustration of the difference in the coordinate systems is given byFIG. 3. The traditional definition of the Fourier-Mellin transform is:

${{f(t)} = {\frac{1}{2\pi \; i}{\int_{c - {i\; \infty}}^{c + {i\; \infty}}{{F(s)}e^{st}\ {s}}}}},{t > 0},$

The Fourier-Mellin transform is invariable in translation, rotation, andscale. The Fourier-Mellin method consists of four steps. First, the FFT(Fast Fourier Transform) of an image is taken. A FFT is a discreteFourier transform algorithm which reduces the number of computationsneeded for N points from (N2) to (2*N*(lg N)), where lg is the base-2logarithm. If the function to be transformed is not harmonically relatedto the sampling frequency, the response of an FFT looks like a sincfunction (although the integrated power is still correct). Aliasing(leakage) can be reduced by apodization using a tapering function.However, aliasing reduction is at the expense of broadening the spectralresponse.

The second step of the Fourier-Mellin transform is involves the step oftaking the Cartesian coordinates and converting them to Log-Polarcoordinates. This allows for a correlation between translation in theFourier-Mellin domain and rotation in Cartesian domain.

Third, the Mellin Transform is taken. The Mellin transform is anintegral transform that and is generally regarded as the multiplicativeversion of the two-sided Laplace transform. The general equation for aMellin transform on an equation f(t) is:

${\left\{ {Mf} \right\} (s)} = {{\phi (s)} = {\int_{0}^{\infty}{x^{s}{f(x)}\ {\frac{x}{x}.}}}}$

Finally, the data from the output is analyzed to determine the point ofmost similarity and adjustments for movement may be made. One example ofan implementation of this Fourier-Mellin approach in a softwareapplication such as Matlab is given in FIG. 4.

Another signal processing method used is Phase Correlation. PhaseCorrelation consists of taking the Fourier Transform of the two imagesand correlating the relative phases to find rotation or scale. Phasecorrelation is another technique that utilizes a Fast Fourier Transformor FFT.

By taking the two dimensional FFT of an image, phase information can bevisualized. One equation used to acquire the FFT of an image is:

$f_{j} = {\sum\limits_{k = 0}^{n - 1}{^{{- 2}\pi \; {{j} \cdot {({k/n})}}}x_{k}}}$

In two dimensions, the x_(k) can be viewed as an n₁×n₂ matrix. Thealgorithm corresponds to first performing the FFT of all the rows andthen of all the columns (or vice versa).

In the phase correlation technique, it is possible to compare the phasesof the two images to detect the difference between the two images. Bydetermining the point where the phases are at the maximum congruency, itis possible to determine the angle of rotation between two images. Byanalysis of the transform, the phase information that is contained in animage is acquired. The change in the phase information holds the key todetermining the rotation angle of the image. The peak in the middle ofthe graph corresponds to the point of most congruency of the phases ofthe two images, and gives the change in angle that the image hasundergone. The phase correlation algorithm was utilized using thefollowing steps. First, the discrete FFT of two images is calculated.Second, the conjugate of the second image is taken. Third, the Fouriertransforms are multiplied together element-wise. Fourth, the product ofthis multiplication is normalized element-wise. Fifth, the normalizedcross power spectrum inverse transform is performed. Sixth, the peak ofthe inverse transform is taken. This step may include using sub-pixelmethods to determine where a peak is found.

In one preferred embodiment, the results from the structured light wereoptimized by maximizing the percentage of the image taken up bystructured light without the structured light exceeding the boundariesof the target image. This preferred upper boundary (i.e. the structuredlight staying within the target image) is a result of the reliance bythe cross-correlation algorithms on a pixel-by-pixel comparison of twoimages. Since the algorithm compares structured light, it is desirableto achieve the best ratio of pixels per structured light element. If thestructured light takes up 100% of the image, no change can be perceivedbetween the structured light and the surrounding environment. If thesize of the structured light is too big (90%), different translationaland rotational motions might take some of the structured light out ofthe field of view of the camera thus contributing to loss of informationcontained in the structured light. On the other hand, if the structuredlight constitutes too little (1%) of the overall image,cross-correlation and Fourier-Mellin algorithms will not be robustenough to perform precise calculations.

In FIG. 5, one preferred embodiment is shown. Light is generated withcoherent laser 520. The light passes through structured light generator530 that is located between light source 520 and conveying light path540. Structured light generator 530 could be implemented as, but notlimited to, a speckle pattern, a spatial filter, a slide, an array oflight emitters, or a spatial light modulator based, for example, on aliquid crystal or a MEMS device. The structured light travels throughconveying path 540 to object to be imaged 550. Conveying path 540 couldbe an image preserving optical fiber, free space, or any medium whichdoes not disrupt the transmission of the structured light. Thestructured light appears on object to be imaged 550. Next the structuredlight is reflected onto return light path 560 which could be an imagepreserving optical fiber, free space, or any medium which does notdisrupt the transmission of the structured light. If return light path560 is free space, image optics have to be correctly determined usinglenses, mirrors or other optical train as would be well known in theart. The light arrives at analyzer 570, which could be a filter orpolarizer before entering detector array 580. In this embodiment,detector array 580 is implemented as a CCD camera. One exemplary partthat could be used is a Digital Rebel XT made by Canon. The structuredlight pattern is used to detect the movement of object 550.

FIG. 6 shows another preferred embodiment. Light is generated withcoherent laser 620. It enters conveying light path 640. Conveying lightpath 640 could be a multimode fiber or any medium that does not disruptthe transmission of the structured light. Inside conveying light path640, a structured light pattern is generated, for example a specklepattern. The structured light appears on object to be imaged 650. Nextthe structured light goes into return light path 660, which could be animage preserving fiber, example-coherent bundle, free space, or throughany medium which does not disrupt the transmission of the structuredlight. If return light path 660 is free space, image optics have to becorrectly determined using lenses, mirrors or other optical train aswould be well known in the art. The structured light enters detectorarray 670 which, in this embodiment, is implemented as a CCD camera. Thestructured light pattern is used to detect the movement of the object650.

In FIG. 7, another preferred embodiment is shown. Light is generatedwith coherent laser 720. The light travels on conveying path 740 tostructured light generator 730 that is located between conveying lightpath 740 and object to be imaged 750. Conveying light path 740 could bean optical fiber, free space, or any medium that does not disrupt thetransmission of the structured light. Structured light generator 730could be implemented as, but not limited to, a speckle pattern, aspatial filter, a slide, an array of light emitters, or a spatial lightmodulator based, for example, on a liquid crystal or a MEMS device. Thestructured light appears on object to be imaged 750. Next the structuredlight goes into return light path 760 which could be an image preservingfiber, example-coherent bundle, free space, or any medium that does notdisrupt the transmission of the structured light. If return light path760 is free space, image optics have to be correctly determined usinglenses, mirrors or other optical train as would be well known in theart. The light arrives at analyzer 770, which could be a filter orpolarizer before entering detector array 780. Detector array 780 isimplemented in this embodiment as a CCD camera. The structured lightpattern is used to detect the movement of object 750.

In FIG. 8, another preferred embodiment is shown. Light is generatedwith coherent laser 820. The light travels on conveying path 840 tostructured light generator 850 that is located on the object. Structuredlight generator 850 is a reflective material that produces structuredlight; an example would be a tag with a high-resolution matrix on it, ora hologram. The structured light appears on object to be imaged 860.Next the structured light goes into return light path 870. If returnlight path 870 is free space, image optics have to be correctlydetermined using lenses, mirrors or other optical train as would be wellknown in the art. The light arrives at analyzer 880, which could be afilter or polarizer before entering detector array 890. The filter canbe responsible for selectively allowing a specified frequency of lightto reach the detector. In this embodiment, detector array 890 isimplemented as a CCD camera. The structured light pattern is used todetect the movement of object 860.

In yet another preferred embodiment, structured light generator 850 is aphysical target with the ability to independently produce a structuredlight pattern. In this embodiment, a matrix of high intensity LEDdevices is arranged in the pattern to transmit a structured light beamto a receiver.

In another embodiment, structured light generator 850 is a tag which hasimpressed on it a laser luminophore such as a polycyclic chemicalcompound that is usually characterized as fluorescent. Fluorophores arealso suitable. Suitable laser luminophores are available as laser pumpeddyes sold for example by Lambda Physik Goettingen, Germany. Typicallaser luminophores display fluorescence in the range of 300 to 2500 nmand have a peak width of about 200 nm. Suitable dyes are applied to areflective tag in a pattern which produces structured light whenilluminated with radiation and wavelengths which produced fluorescence.Light sources such as laser light sources emitting in the 200 to 600 nmrange are suitable. The most preferred sources include XeCl-excimerlasers (309 nm), nitrogen lasers (337 nm) and Nd:YAG (335 nm). Otherpreferred light sources LEDs which generally emit light in a wavelengthrange of about 400 to 600 nm. Chemical compounds useful as fluorophoresin this embodiment include polycyclic hydrocarbons includingcatacondensed and pericondensed aromatics, heterocyclic hydrocarbons,including condensed and substituted indoles, oxazoles, oxadiazoles andfurnin compounds and xanthono and xanthonone derivatives includingcondensed systems, acids and salts. Representative laser luminophoreswhich are useful in this embodiment include p-quatraphenyl, perchloratebenzoic acid, monohydrochloride. Of course, other laser luminophores andfluorophores will also suffice. Representative laser luminophores whichare useful in this embodiment include p-quatraphenyl, perchloratebenzoic acid, monohydrochloride. Of course, other laser luminophoreswill also suffice.

In yet another embodiment, visible dyes and invisible dyes such as laserluminophores or flurophores are used on a tag in different or similarpatterns. Illuminating radiation of different frequencies can then beused to produce reflectances in structured light of differentfrequencies so that changes in motion of the structured light generatorcan be detected in two frequencies at the same time. The redundanciesare available allow more accurate determination of movement of thestructured light generator.

In FIG. 18, another preferred embodiment is shown. Structured lightgenerator 1850 in this embodiment is a physical tag having threedifferent patterns impressed on it with dyes including a visible dye, alaser luminophore dye, and a flurophore. Each of the patterns isdifferent. Light is generated corresponding to the first dye by lightsource 1805. Reflected light from light source 1805 impinges onstructured light generator 1850 and is reflected toward filter 1810 andreceiver 1815. Filter 1810 is designed to tune the light received fromstructured light generator 1850 to a frequency receptive to the laserluminophore dye. Light source 1820 produces light at a certain differentfrequency which impinges on structured light generator 1850 and isreflected toward filter 1825 and receiver 1830. Filter 1825 is designedto tune the light from light source 1820 to the frequency of theflurophore included in structured light generator 1850.

Light source 1830 generates light at a third frequency which impinges onstructured light generator 1850 and is reflected at a certain visiblefrequency toward filter 1835 and receiver 1840. Filter 1835 is designedto tune the reflected light from tag 1850 to a visible frequency. Eachof the receivers is capable of registering the pattern produced by aspecific dye on structured light generator 1850.

In a block diagram of head tracking apparatus 910 providing active realtime feedback to the measurement fields of MRI instrument 980 is shown.Light is generated with head tracking apparatus 910. The structuredlight travels on conveying path 920 to object 930 under analysis. Nextthe structured light goes into return light path 940 which could be animage preserving fiber, example-coherent bundle, or free space. Thestructured light is registered at head tracking apparatus 910 and sentto interface 960 between MRI instrument 980 and the head trackingapparatus via information-carrying channel 950. Interface 960 can beimplemented as a computer. Interface 960 calculates the change inposition of object 930 under analysis and sends the information to MRIinstrument 980 via information carrying channel 970. MRI instrument 980adjusts the fields according to the new position information. This isaccomplished in real time between successive scans of the MRIinstrument.

In FIG. 10, a flow chart illustrating the method of head tracking isshown. In the first phase structured light is generated 1010. In thesecond phase, structured light is used to measure position 1020 of theobject. In the third phase, the object moves and the received structuredlight 1030 pattern changes. In the fourth phase, the change in thereceived structured light pattern is calculated 1040.

The object for the structured light to be focused on may be created in anumber of ways depending on the embodiment chosen. One preferredembodiment is the use of a random monochromatic pattern that is used asa target. FIG. 11 is an example of one target that may be used tooptimize the results from structured light. FIG. 12 is an illustrationof the technique of placing this type of target pattern or tag onto apatient's forehead. FIG. 13 is an illustration of the technique ofprojecting this type of pattern onto a patient's forehead. One anexemplary part that can be used for projection is an EP 751 DLP made byOptima.

In FIG. 14, a flow chart illustrating the method of providing activefeedback to the measurement fields of the MRI based on the head trackingdata is shown. In the first phase structured light is generated 1410. Inthe second phase, structured light is used to measure position 1420 ofthe object. In the third phase, the object moves and the receivedstructured light 1430 pattern changes. In the fourth phase, the changein the received structured light pattern is calculated 1440. In thefifth phase, the calculated change in the position of the object is sentto MRI instrument interface 1450. In the sixth phase, interface 1450communicates with the MRI instrument to adjust the fields of the MRI toimprove scan 1460. This is accomplished in real time between successivescans of the MRI instrument.

FIG. 15 is a flow chart illustrating one implementation of theTranslation Detection Algorithm. Structured light is projected on target1505. A first image is recorded (image N) 1510. Another image is thenrecorded (image N+1) 1515. Host computer cross-correlates images (imagesN and N+1) 1520. The host computer finds the peak of cross-correlationthat corresponds to the point of ‘most similarity’ 1525. The hostcomputer infers transformation data from the location of thecross-correlation peak and sends results to the MRI 1530. The next imageis recorded (image N+2) 1535. Host computer cross-correlates images N+2and N+1 1540. Find the peak of cross-correlation that corresponds to thepoint of ‘most similarity’ 1545. Infer transformation data from thelocation of the cross-correlation peak and send results to MRI 1550.

FIG. 16 is a flowchart illustrating a possible method of calibration.Structured light is projected on target 1605. The first step is for afirst image is recorded 1610. The next step is for a second image isrecorded (image N+1) 1615. The host computer cross-correlates acquiredimages 1620. The coordinates from image N are assigned to be at a pointbased in Cartesian coordinate system 1625. This point can be used as areference point for all further image calculation.

FIG. 17 is a flowchart illustrating another method of using structuredlight to correct for motion data. In this embodiment, there are threeseparate components: a host connected to an optical receiver capable ofreceiving, processing, and cross-correlating images, a network computerto create a motion file based on information obtained from the hostcomputer, and an MRI controller capable of interfacing with the networkcomputer to accept data from the network computer and correct MRIresults based upon data obtained by the network controller.

In the embodiment that is illustrated by FIG. 17, the first step is toproject a predetermined pattern of structured light onto target, wherethe area of the structured light is less than the area of the pattern,and where the structured light falls completely within the target 1705.Structured light is reflected off the target and into the host opticalreceiver, and an image (image N) is captured by host optical receiverand time stamped 1710. The next step is for a second image to berecorded (image N+N) and time stamped 1715. The host computercross-correlates images (images N and N+1) and finds the peak ofcross-correlation that corresponds to the point of ‘most similarity’1720. The host computer infers transformation data from the location ofthe cross-correlation peak and sends results to network computer 1725.The network computer creates a motion file indicating where motion hasoccurred, what type of motion has occurred, and the magnitude of themotion and creates correction file 1730. The network computer transmitsthe correction file to MRI controller 1735. The MRI controller correctsthe MRI results to adjust for the motion detected by the host computerby using the time stamps on each image taken by the MRI to the motiondata time stamped by host computer 1740. If the MRI scan is complete,the host turns off structured light 1760. If the MRI scan is notcomplete, image M is recorded by host and time stamped 1750. The hostcomputer cross-correlates images (images N and M) and finds the peak ofcross-correlation that corresponds to the point of ‘most similarity’1755. The host computer infers transformation data from the location ofthe cross-correlation peak from the original image N and the new image Mand sends results to network computer 1725.

It is envisioned that the embodiment illustrated by FIG. 17 could bemodified to allow for the three components (e.g. network computer, hostcomputer, and MRI controller) to be integrated into one or morecomponents. It is further envisioned that one computer to accomplish oneor more of the tasks, i.e. a central computer both capture and processimages and transmit the data directly to an MRI. It is furtherenvisioned by the inventors that the comparison in images could be madefrom the each previous image in the sequence rather than from the firstimage to give a clearer view of subtle changes in movement.

It is further envisioned that there are other methods to use structuredlight patterns; i.e. electronic detection patterns such as sensorsattached to the patient's head that could be used as an alternative tooptical receivers. Moreover, any number of different types of lightsource may be used to project light, including, but not limited to,strobe lights. It is further envisioned that in some embodiments targetitself emanate light by the use of a target that emits light directlyinto an optical receiver.

While this invention has been described in reference to illustrativeembodiments, this description is not intended to be construed in alimiting sense. Various modifications and combinations of theillustrative embodiments, as well as other embodiments of the invention,will be apparent to persons skilled in the art upon reference to thedescription. It is therefore intended that the appended claims encompassany such modifications or embodiments.

As will be recognized by those skilled in the art, the innovativeconcepts described in the present application can be modified and variedover a tremendous range of applications, and accordingly the scope ofpatented subject matter is not limited by any of the specific exemplaryteachings given.

1. A motion tracking method for determining the location and orientationof at least one object moving in space, comprising the steps of:generating structured light; projecting structured light onto a target;receiving return light from the target through an optical receiver;converting the return light into coordinate data; and translating thecoordinate data into motion information.
 2. The method of claim 1,further comprising the step of adjusting an MRI scan to compensate forthe motion information.
 3. The method of claim 1, further comprising thestep of placing the target inside an MRI machine.
 4. The method of claim1, further comprising the step of coordinating the target with a patientsurface.
 5. The method of claim 1, further comprising the step oforiginating the structured light source within the MRI apparatus.
 6. Themethod of claim 1, further comprising the step of generating thestructured light from a source selected from a group consisting of: alow-intensity pulsed light source, or continuous light source.
 7. Themethod of claim 1 further comprising the step of sensitizing the opticalreceiver to at least one non-visible frequency.
 8. The method of claim2, further comprising the step of transmitting the motion information toa computer for alignment.
 9. The method of claim 2, further comprisingthe step of guiding an interventional procedure with the motioninformation.
 10. The method of claim 2, further comprising the step ofevaluating changes in brain images with the motion information acquiredover time periods selected from the group consisting of minutes, hours,days, weeks, and months, or a combination thereof.
 11. A method fordetecting position changes in movement during a medical imagingoperation, comprising the steps of: generating a structured lightcoupled to a target; receiving the structured light through an opticalreceiver; converting the structured light into coordinate data;measuring an initial position of a patient with the structured light;measuring a second position of the patient with the structured light;determining a position shift between the initial position and secondposition; and transmitting the position shift to a medical imagingdevice.
 12. The method of claim 11, further comprising the step ofadjusting the medical device to correct for the position shift.
 13. Themethod of claim 11, wherein the step of transmitting further comprises:transmitting the position shift to an MRI device.
 14. The method ofclaim 11, wherein the step of generating structured light furthercomprises the steps of: providing a structured light projector; and,focusing the structured light projector on a patient.
 15. The method ofclaim 11, wherein the step of generating structured light furthercomprises: attaching a reflective pattern to a patient; and irradiatingthe pattern with a coherent light source.
 16. The method of claim 11,wherein the step of receiving includes the step of pulling a CCD camera.17. The method of claim 15, wherein the step of receiving includesreceiving at least two frequencies of light.
 18. The method of claim 11,wherein the step of generating includes the step of using a laserfocused through a filter that acts as a structured light generator. 19.A method of analyzing movement of an object comprising the steps of:receiving structured light from the object; and analyzing the structuredlight to determine movement of the object.